mlsanityData to 14 Oct 2026

Aware First, Think Less: Dynamic Boundary Self-Awareness Drives Extreme Reasoning Efficiency in Large Language Models

arXiv 2508.11582 ↗First posted 15 Aug 2025cs.CL cs.AIcited by 13

On the map: LLM reasoning → Test-time compute and efficient thinking

Abstract (arXiv)

Recent advancements in large language models (LLMs) have greatly improved their capabilities on complex reasoning tasks through Long Chain-of-Thought (CoT). However, this approach often results in substantial redundancy, impairing computational efficiency and causing significant delays in real-time applications. To improve the efficiency, current methods often rely on human-defined difficulty priors, which do not align with the LLM's self-awared difficulty, leading to inefficiencies. In this paper, we introduce the Dynamic Reasoning-Boundary Self-Awareness Framework (DR. SAF), which enables models to dynamically assess and adjust their reasoning depth in response to problem complexity. DR. SAF integrates three key components: Boundary Self-Awareness Alignment, Adaptive Reward Management, and a Boundary Preservation Mechanism. These components allow models to optimize their reasoning processes, balancing efficiency and accuracy without compromising performance. Our experimental results demonstrate that DR. SAF achieves a 49.27% reduction in total response tokens with minimal loss in accuracy. The framework also delivers a 6.59x gain in token efficiency and a 5x reduction in training time, making it well-suited to resource-limited settings. During extreme training, DR. SAF can even surpass traditional instruction-based models in token efficiency with more than 16% accuracy improvement.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning. Builds on 45 of its 59 references that are in the map. First posted on arXiv 15 Aug 2025.

Builds on

45 of 59 references.

Closest ancestors · 22

Same direction, newest first

  1. 2025Fast on the Easy, Deep on the Hard: Efficient Reasoning via Powered Length Penalty12 Jun
  2. 2025TL;DR: Too Long, Do Re-weighting for Efficient LLM Reasoning Compression3 Jun
  3. 2025AutoL2S: Auto Long-Short Reasoning for Efficient Large Language Models28 May
  4. 2025Don't Think Longer, Think Wisely: Optimizing Thinking Dynamics for Large Reasoning Models27 May
  5. 2025AdaCtrl: Towards Adaptive and Controllable Reasoning via Difficulty-Aware Budgeting24 May
  6. 2025Prolonged Reasoning Is Not All You Need: Certainty-Based Adaptive Routing for Efficient LLM/MLLM Reasoning21 May
  7. 2025ThinkSwitcher: When to Think Hard, When to Think Fast20 May
  8. 2025AdaptThink: Reasoning Models Can Learn When to Think19 May
  9. 2025AdaCoT: Pareto-Optimal Adaptive Chain-of-Thought Triggering via Reinforcement Learning17 May
  10. 2025Learning When to Think: Shaping Adaptive Reasoning in R1-Style Models via Multi-Stage RL16 May
  11. 2025ConCISE: Confidence-guided Compression in Step-by-step Efficient Reasoning8 May
  12. 2025Dynamic Early Exit in Reasoning Models22 Apr
10 more
  1. 2025Efficient Reasoning Models: A Survey15 Apr
  2. 2025A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?31 Mar
  3. 2025Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models20 Mar
  4. 2025DAST: Difficulty-Adaptive Slow-Thinking for Large Reasoning Models6 Mar
  5. 2025Dynamic Parallel Tree Search for Efficient LLM Reasoning22 Feb
  6. 2025Demystifying Long Chain-of-Thought Reasoning in LLMs5 Feb
  7. 2025O1-Pruner: Length-Harmonizing Fine-Tuning for O1-Like Reasoning Pruning22 Jan
  8. 2024Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs30 Dec
  9. 2024Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systems12 Dec
  10. 2024O1 Replication Journey: A Strategic Progress Report -- Part 18 Oct

Other references · 23

  1. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  2. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  3. 2024Qwen2.5 Technical Reportcited by 4,942
  4. 2024HybridFlow: A Flexible and Efficient RLHF Frameworkcited by 2,057
  5. 2024OpenAI o1 System Cardcited by 1,244
  6. 2025Kimi k1.5: Scaling Reinforcement Learning with LLMscited by 1,210
  7. 2024Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvementcited by 1,090
  8. 2024RouteLLM: Learning to Route LLMs with Preference Datacited by 682
  9. 2025Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Modelscited by 384
  10. 2024Hybrid LLM: Cost-Efficient and Quality-Aware Query Routingcited by 372
  11. 2025From System 1 to System 2: A Survey of Reasoning Large Language Modelscited by 320
  12. 2025DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoningcited by 280
11 more
  1. 2025Training Language Models to Reason Efficientlycited by 247
  2. 2025OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automationcited by 131
  3. 2025Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuningcited by 123
  4. 2025Efficient Reinforcement Finetuning via Adaptive Curriculum Learningcited by 93
  5. 2024System-1.x: Learning to Balance Fast and Slow Planning with Language Modelscited by 35
  6. 2025Debate Only When Necessary: Adaptive Multiagent Collaboration for Efficient LLM Reasoningcited by 27
  7. 2025Text2World: Benchmarking Large Language Models for Symbolic World Model Generationcited by 26
  8. 2025Electronic Circuit Principles of Large Language Modelscited by 4
  9. 2025RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoningcited by 4
  10. 2025MLDebugging: Towards Benchmarking Code Debugging Across Multi-Library Scenarioscited by 3
  11. 2025MPCC: A Novel Benchmark for Multimodal Planning with Complex Constraints in Multimodal Large Language Modelscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 9.

13 papers.

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  1. 2026VideoAuto-R1: Video Auto Reasoning via Thinking Once, Answering Twicecited by 21
  2. 2025When to Reason: Semantic Router for vLLMcited by 12
  3. 2026Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Modelscited by 8
  4. 2026On-Policy Supervised Fine-Tuning for Efficient Reasoningcited by 4
  5. 2026Stable Adaptive Thinking via Advantage Shaping and Length-Aware Gradient Regulationcited by 4
  6. 2026Compress the Easy, Explore the Hard: Difficulty-Aware Entropy Regularization for Efficient LLM Reasoningcited by 4
  7. 2025Verifying Meta-Awareness via Predictive Rewards in Reasoning Modelscited by 3
  8. 2026Draft-Thinking: Learning Efficient Reasoning in Long Chain-of-Thought LLMscited by 3
  9. 2025Beware of Reasoning Overconfidence: Pitfalls in the Reasoning Process for Multi-solution Taskscited by 2
  10. 2026PACE: Prefix-Protected and Difficulty-Aware Compression for Efficient Reasoningcited by 2
  11. 2025Boundary-to-Region Supervision for Offline Safe Reinforcement Learningcited by 1
  12. 2026Nice Fold or Hero Call: Learning Budget-Efficient Thinking for Adaptive Reasoningcited by 0
1 more
  1. 2026Efficient Chain-of-Modality Reasoning via Progressive Compression for Spoken Language Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Enhancing LLM Reliability via Explicit Knowledge Boundary Modeling
  2. 2025Beyond Chains of Thought: Benchmarking Latent-Space Reasoning Abilities in Large Language Models
  3. 2025Advancing Reasoning in Large Language Models: Promising Methods and Approaches
  4. 2025Longer Context, Deeper Thinking: Uncovering the Role of Long-Context Ability in Reasoning
  5. 2026Improving reasoning at inference time via uncertainty minimisation
  6. 2024Large Language Models Can Self-Improve in Long-context Reasoning
  7. 2026Reasoning Shift: How Context Silently Shortens LLM Reasoning
  8. 2025Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models